Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Abstract

Capillary blood vessels in the body partake in the exchange of gas and nutrients with tissues. They are interconnected via multiple vascular junctions forming the microvascular network. Distributions of blood flow and red cells (RBCs) in such networks are spatially uneven and vary in time. Since they dictate the pathophysiology of tissues, their knowledge is important. Theoretical models used to obtain flow and RBC distribution in large networks have limitations as they treat each vessel as a one-dimensional segment and do not explicitly consider cell–cell and cell–vessel interactions. High-fidelity computational models that accurately model each individual RBC are computationally too expensive to predict haemodynamics in large vascular networks and over a long time. Here we investigate the applicability of machine learning (ML) techniques to predict blood flow and RBC distributions in physiologically realistic vascular networks. We acquire data from high-fidelity simulations of deformable RBC suspension flowing in the networks. With the flow and haematocrit specified at an inlet of vasculature, the ML models predict the time-averaged flow rate and RBC distributions in the entire network, time-dependent flow rate and haematocrit in each vessel and vascular bifurcation in isolation over a long time, and finally, simultaneous spatially and temporally evolving quantities through the vessel hierarchy in the networks.

Predicting capillary vessel network hemodynamics in silico by machine learning

“…However, these additional features can be considered in future models by introducing appropriate geometric parameters as additional inputs. Also, the ML models are not vasculature-specific; vasculatures can be swapped for training and testing ( 42 ). Furthermore, the model inputs are local concentration and velocity profiles for each vascular component; so even though the DSR data is obtained for a fixed inflow hematocrit and flow rate, the model is not limited to this.…”

“…Recently, our group has developed an ML model to predict blood flow rate and vessel-averaged RBC concentration in the microvascular network ( 42 ). This prior model was a spatially 1D model as the velocity and concentration profiles over a vessel cross-section were not considered.…”